Book Image

Hands-On Machine Learning with C++

By : Kirill Kolodiazhnyi
Book Image

Hands-On Machine Learning with C++

By: Kirill Kolodiazhnyi

Overview of this book

C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), showing you how to use C++ libraries to get the most out of your data. This book makes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement supervised and unsupervised ML algorithms through real-world examples. This book will get you hands-on with tuning and optimizing a model for different use cases, assisting you with model selection and the measurement of performance. You’ll cover techniques such as product recommendations, ensemble learning, and anomaly detection using modern C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Next, you’ll explore neural networks and deep learning using examples such as image classification and sentiment analysis, which will help you solve various problems. Later, you’ll learn how to handle production and deployment challenges on mobile and cloud platforms, before discovering how to export and import models using the ONNX format. By the end of this C++ book, you will have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.
Table of Contents (19 chapters)
Section 1: Overview of Machine Learning
Section 2: Machine Learning Algorithms
Section 3: Advanced Examples
Section 4: Production and Deployment Challenges

Sentiment Analysis with Recurrent Neural Networks

Currently, the recurrent neural network (RNN) is one of the most well-known and practical approaches used to construct deep neural networks. They are designed to process time-series data. Typically, data of this nature is found in the following tasks:

  • Natural language text processing, such as text analysis and automatic translation
  • Automatic speech recognition
  • Video processing, for predicting the next frame based on previous frames, and for recognizing emotions
  • Image processing, for generating image descriptions
  • Time series analysis, for predicting fluctuations in exchange rates or company stock prices

In recurrent networks, communications between elements form a directed sequence. Thanks to this, it becomes possible to process a time series of events or sequential spatial chains. Unlike multilayer perceptrons, recurrent networks...